This invention generally relates to a method for collecting machine vibration data, and more particularly to a method for analyzing vibration signatures to predict and to detect changes in machinery condition.
By design, machinery having rotative elements, which are couplingly connected, experience vibratory motion. This vibratory motion may be generated by such rotative elements as the following: machine bearings, such as bearing races, or defective ball bearings; misalignment of machine assemblies, such as gears, motors, or shafts; and imbalance of machine assemblies, such as motors, rotors, gears, pistons and fans. The vibratory motion of such machine assemblies may be expressed in the form of a vibration signature, vibration footprint or "footprint", which may be graphically illustrated.
The present and future condition of machinery may be determined and predicted by analyzing predetermined vibration signatures of individual machinery. Determining the present and future condition of machinery is essential for maintaining such machinery on line and capable of contributing to an essential manufacturing process. The machinery to be studied may include rotating type machinery, such as but not limited to rotary screw type air compressors. Such rotary screw type air compressors typically supply the entire pneumatic requirements for a manufacturing facility. In such an example, if the rotary screw air compressors fail in their essential function, production at the manufacturing facility will most likely cease until such time as the fault condition is remedied or a back up pneumatic supply is located. This, of course, may cause a great loss of revenue for the affected manufacturing facility. Ideally, a potential fault condition of a machine should be identified as early as possible to permit a facility manager to schedule "down" time and machine maintenance in a cost effective manner.
In an effort to avoid the loss of revenue caused by "down" equipment, manufacturing facility managers have, in the past, employed independent firms that specialize in the field of predictive vibration monitoring of machinery. It is the purpose of such firms to supply personnel to a manufacturing facility for the purpose of performing on-site vibration monitoring. As is well known, in order to effectively perform predictive vibration monitoring of machinery, the "normal" vibration signatures of all the rotative components must be known before predictive vibration monitoring is performed. These "normal" vibration signatures of the rotative components serve as a benchmark from which to evaluate all other vibration signatures. Notwithstanding the foregoing, typically such independent firms performing predictive vibration monitoring do not know the "normal" vibration signatures of the machines to be monitored. Without the knowledge of such "normal" vibration signatures, predictive vibration monitoring programs may produce extremely inaccurate results, which is a problem presently plaguing this field. Such inaccurate results cause unnecessary repair of machines that are otherwise in sound operating condition, and cause the owners of such machines to file meritless warranty claims against the manufacturer of such machines.
As may be appreciated by one skilled in the art, any collection of vibration data for the purpose of predictive vibration monitoring must be performed under equal machine conditions to achieve accurate results. Present methods of collecting vibration data accomplish such data collection absent any accurate correlation to the running state of the monitored machine. For example, on a predetermined date, vibration data may be collected for a compressor under compressor loaded conditions. Thereafter vibration data may be collected for a compressor under compressor unloaded conditions. Inaccuracy will occur if the dissimilar collected vibration data is compared to predict the present and future condition of the monitored machine.
In addition to the foregoing, present methods of vibration data collection do not permit any integration between the vibration monitoring and a microprocessor based control system of the monitored machine. This lack of integration prevents any continuous logging of machine vibration data which would permit a significantly more accurate analysis of any gathered vibration data.
The foregoing illustrates limitations known to exist in present methods for collecting vibration data for the purpose of predicting and detecting changes in machinery condition. Thus, it is apparent that it would be advantageous to provide an alternative directed to overcoming one or more of the limitations set forth above. Accordingly, a suitable alternative is provided including features more fully disclosed hereinafter.